Results 21 to 30 of about 20,022 (282)
Bayesian Test of Significance for Conditional Independence: The Multinomial Model
Entropy, 2014 Conditional independence tests have received special attention lately in machine learning and computational intelligence related literature as an important indicator of the relationship among the variables used by their models.
Pablo de Morais Andrade +2 more
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Inference Attacks on Genomic Data Based on Probabilistic Graphical Models
Big Data Mining and Analytics, 2020 The rapid progress and plummeting costs of human-genome sequencing enable the availability of large amount of personal biomedical information, leading to one of the most important concerns — genomic data privacy. Since personal biomedical data are highly
Zaobo He, Junxiu Zhou
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SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting
Forecasting, 2022 Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR model, which incorporates machine learning (ML) into the epidemiological ...
Roberto Vega +2 more
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Deep Probabilistic Graphical Modeling
CoRR, 2020 Probabilistic graphical modeling (PGM) provides a framework for formulating an interpretable generative process of data and expressing uncertainty about unknowns, but it lacks flexibility. Deep learning (DL) is an alternative framework for learning from data that has achieved great empirical success in recent years.
openaire +3 more sources
Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
Physical Review X, 2017 Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions.
Marcello Benedetti +3 more
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From Probabilistic Graphical Models to Generalized Tensor Networks for Supervised Learning
IEEE Access, 2020 Tensor networks have found a wide use in a variety of applications in physics and computer science, recently leading to both theoretical insights as well as practical algorithms in machine learning.
Ivan Glasser +2 more
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An Order-Independent Algorithm for Learning Chain Graphs
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021 LWF chain graphs combine directed acyclic graphs and undirected graphs. We propose a PC-like algorithm, called PC4LWF, that finds the structure of chain graphs under the faithfulness assumption to resolve the problem of scalability of the proposed ...
Mohammad Ali Javidian +2 more
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On a Class of Tensor Markov Fields
Entropy, 2020 Here, we introduce a class of Tensor Markov Fields intended as probabilistic graphical models from random variables spanned over multiplexed contexts. These fields are an extension of Markov Random Fields for tensor-valued random variables.
Enrique Hernández-Lemus
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Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons. [PDF]
PLoS Computational Biology, 2011 An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in ...
Dejan Pecevski +2 more
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On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models. [PDF]
Adv Neural Inf Process SystDeep neural networks (DNNs) lack the precise semantics and definitive probabilistic interpretation of probabilistic graphical models (PGMs). In this paper, we propose an innovative solution by constructing infinite tree-structured PGMs that correspond ...
Li B +4 more
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